OpenRaiser/Intern-Atlas huggingface dataset:
OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence
Venue: arXiv cs.AI 2025
Year: 2025

The rapid advancement of multimodal large language models (LLMs) has opened new frontiers in artificial intelligence, enabling the integration of diverse large-scale data types such as text, images, and spatial information. In this paper, we explore the potential of multimodal LLMs (MLLM) for geospatial artificial intelligence (GeoAI), a field that leverages spatial data to address challenges in domains including Geospatial Semantics, Health Geography, Urban Geography, Urban Perception, and Remote Sensing. We propose a MLLM (OmniGeo) tailored to geospatial applications, capable of processing and analyzing heterogeneous data sources, including satellite imagery, geospatial metadata, and textual descriptions. By combining the strengths of natural language understanding and spatial reasoning, our model enhances the ability of instruction following and the accuracy of GeoAI systems. Results demonstrate that our model outperforms task-specific models and existing LLMs on diverse geospatial tasks, effectively addressing the multimodality nature while achieving competitive results on the zero-shot geospatial tasks. Our code will be released after publication.
JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse
Venue: arXiv cs.CV 2025
Year: 2025

Recently, action-based decision-making in open-world environments has gained significant attention. Visual Language Action (VLA) models, pretrained on large-scale web datasets, have shown promise in decision-making tasks. However, previous work has primarily focused on action post-training, often neglecting enhancements to the foundational model itself. In response, we introduce a novel approach, Act from Visual Language Post-Training, which refines Visual Language Models (VLMs) through visual and linguistic guidance in a self-supervised manner. This enhancement improves the models' capabilities in world knowledge, visual recognition, and spatial grounding in open-world environments. Following the above post-training paradigms, we obtain the first VLA models in Minecraft that can follow human instructions on over 1k different atomic tasks, including crafting, smelting, cooking, mining, and killing. Our experiments demonstrate that post-training on non-trajectory tasks leads to a significant 40% improvement over the best agent baseline on a diverse set of atomic tasks. Furthermore, we demonstrate that our approach surpasses traditional imitation learning-based policies in Minecraft, achieving state-of-the-art performance. We have open-sourced the code, models, and datasets to foster further research. The project page can be found in https://craftjarvis.github.io/JarvisVLA.
Reinforcement Learning-based Heuristics to Guide Domain-Independent Dynamic Programming
Venue: arXiv cs.AI 2025
Year: 2025

Domain-Independent Dynamic Programming (DIDP) is a state-space search paradigm based on dynamic programming for combinatorial optimization. In its current implementation, DIDP guides the search using user-defined dual bounds. Reinforcement learning (RL) is increasingly being applied to combinatorial optimization problems and shares several key structures with DP, being represented by the Bellman equation and state-based transition systems. We propose using reinforcement learning to obtain a heuristic function to guide the search in DIDP. We develop two RL-based guidance approaches: value-based guidance using Deep Q-Networks and policy-based guidance using Proximal Policy Optimization. Our experiments indicate that RL-based guidance significantly outperforms standard DIDP and problem-specific greedy heuristics with the same number of node expansions. Further, despite longer node evaluation times, RL guidance achieves better run-time performance than standard DIDP on three of four benchmark domains.
DreamTexture: Shape from Virtual Texture with Analysis by Augmentation
Venue: arXiv cs.CV 2025
Year: 2025

DreamFusion established a new paradigm for unsupervised 3D reconstruction from virtual views by combining advances in generative models and differentiable rendering. However, the underlying multi-view rendering, along with supervision from large-scale generative models, is computationally expensive and under-constrained. We propose DreamTexture, a novel Shape-from-Virtual-Texture approach that leverages monocular depth cues to reconstruct 3D objects. Our method textures an input image by aligning a virtual texture with the real depth cues in the input, exploiting the inherent understanding of monocular geometry encoded in modern diffusion models. We then reconstruct depth from the virtual texture deformation with a new conformal map optimization, which alleviates memory-intensive volumetric representations. Our experiments reveal that generative models possess an understanding of monocular shape cues, which can be extracted by augmenting and aligning texture cues -- a novel monocular reconstruction paradigm that we call Analysis by Augmentation.
Computing Lindahl Equilibrium for Public Goods with and without Funding Caps
Venue: arXiv cs.GT 2025
Year: 2025

Lindahl equilibrium is a solution concept for allocating a fixed budget across several divisible public goods. It always lies in the weak core, meaning that the equilibrium allocation satisfies desirable stability and proportional fairness properties. We consider a model where agents have separable linear utility functions over the public goods, and the output assigns to each good an amount of spending, summing to at most the available budget. In the uncapped setting, each of the public goods can absorb any amount of funding. In this case, it is known that Lindahl equilibrium is equivalent to maximizing Nash social welfare, and this allocation can be computed by a public-goods variant of the proportional response dynamics. We introduce a new convex programming formulation for computing this solution and show that it is related to Nash welfare maximization through double duality and reformulation. We then show that the proportional response dynamics is equivalent to running mirror descent on our new formulation. Our new formulation has similarities to Shmyrev's convex program for Fisher market equilibrium. In the capped setting, each public good has an upper bound on the amount of funding it can receive, which is a type of constraint that appears in fractional committee selection and participatory budgeting. In this setting, existence of Lindahl equilibrium was only known via fixed-point arguments. The existence of an efficient algorithm computing one has been a long-standing open question. We prove that our new convex program continues to work when the cap constraints are added, and its optimal solutions are Lindahl equilibria. Thus, we establish that approximate Lindahl equilibrium can be efficiently computed. Our result also implies that approximately core-stable allocations can be efficiently computed for the class of separable piecewise-linear concave (SPLC) utilities.
Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have demonstrated remarkable capabilities in complex tasks. Recent advancements in Large Reasoning Models (LRMs), such as OpenAI o1 and DeepSeek-R1, have further improved performance in System-2 reasoning domains like mathematics and programming by harnessing supervised fine-tuning (SFT) and reinforcement learning (RL) techniques to enhance the Chain-of-Thought (CoT) reasoning. However, while longer CoT reasoning sequences improve performance, they also introduce significant computational overhead due to verbose and redundant outputs, known as the "overthinking phenomenon". In this paper, we provide the first structured survey to systematically investigate and explore the current progress toward achieving efficient reasoning in LLMs. Overall, relying on the inherent mechanism of LLMs, we categorize existing works into several key directions: (1) model-based efficient reasoning, which considers optimizing full-length reasoning models into more concise reasoning models or directly training efficient reasoning models; (2) reasoning output-based efficient reasoning, which aims to dynamically reduce reasoning steps and length during inference; (3) input prompts-based efficient reasoning, which seeks to enhance reasoning efficiency based on input prompt properties such as difficulty or length control. Additionally, we introduce the use of efficient data for training reasoning models, explore the reasoning capabilities of small language models, and discuss evaluation methods and benchmarking. Project website: https://github.com/Eclipsess/Awesome-Efficient-Reasoning-LLMs
Multimodal Transformer Models for Turn-taking Prediction: Effects on Conversational Dynamics of Human-Agent Interaction during Cooperative Gameplay
Venue: arXiv cs.HC 2025
Year: 2025

This study investigates multimodal turn-taking prediction within human-agent interactions (HAI), particularly focusing on cooperative gaming environments. It comprises both model development and subsequent user study, aiming to refine our understanding and improve conversational dynamics in spoken dialogue systems (SDSs). For the modeling phase, we introduce a novel transformer-based deep learning (DL) model that simultaneously integrates multiple modalities - text, vision, audio, and contextual in-game data to predict turn-taking events in real-time. Our model employs a Crossmodal Transformer architecture to effectively fuse information from these diverse modalities, enabling more comprehensive turn-taking predictions. The model demonstrates superior performance compared to baseline models, achieving 87.3% accuracy and 83.0% macro F1 score. A human user study was then conducted to empirically evaluate the turn-taking DL model in an interactive scenario with a virtual avatar while playing the game "Dont Starve Together", comparing a control condition without turn-taking prediction (n=20) to an experimental condition with our model deployed (n=40). Both conditions included a mix of English and Korean speakers, since turn-taking cues are known to vary by culture. We then analyzed the interaction quality, examining aspects such as utterance counts, interruption frequency, and participant perceptions of the avatar. Results from the user study suggest that our multimodal turn-taking model not only enhances the fluidity and naturalness of human-agent conversations, but also maintains a balanced conversational dynamic without significantly altering dialogue frequency. The study provides in-depth insights into the influence of turn-taking abilities on user perceptions and interaction quality, underscoring the potential for more contextually adaptive and responsive conversational agents.
Analysis of Distracted Pedestrians Crossing Behavior: An Immersive Virtual Reality Application
Venue: arXiv cs.HC 2025
Year: 2025

Pedestrian safety is a critical public health priority, with pedestrian fatalities accounting for 18% of all U.S. traffic deaths in 2022. The rising prevalence of distracted walking, exacerbated by mobile device use, poses significant risks at signalized intersections. This study utilized an immersive virtual reality (VR) environment to simulate real-world traffic scenarios and assess pedestrian behavior under three conditions: undistracted crossing, crossing while using a mobile device, and crossing with Light-emitting diode (LED) safety interventions. Analysis using ANOVA models identified speed and mobile-focused eye-tracking as significant predictors of crossing duration, revealing how distractions impair situational awareness and response times. While LED measures reduced delays, their limited effectiveness highlights the need for integrated strategies addressing both behavioral and physical factors. This study showcases VRs potential to analyze complex pedestrian behaviors, offering actionable insights for urban planners and policymakers aiming to enhance pedestrian safety.
Towards properly implementing Theory of Mind in AI systems: An account of four misconceptions
Venue: arXiv cs.HC 2025
Year: 2025

The search for effective collaboration between humans and computer systems is one of the biggest challenges in Artificial Intelligence. One of the more effective mechanisms that humans use to coordinate with one another is theory of mind (ToM). ToM can be described as the ability to `take someone else's perspective and make estimations of their beliefs, desires and intentions, in order to make sense of their behaviour and attitudes towards the world'. If leveraged properly, this skill can be very useful in Human-AI collaboration. This introduces the question how we implement ToM when building an AI system. Humans and AI Systems work quite differently, and ToM is a multifaceted concept, each facet rooted in different research traditions across the cognitive and developmental sciences. We observe that researchers from artificial intelligence and the computing sciences, ourselves included, often have difficulties finding their way in the ToM literature. In this paper, we identify four common misconceptions around ToM that we believe should be taken into account when developing an AI system. We have hyperbolised these misconceptions for the sake of the argument, but add nuance in their discussion. The misconceptions we discuss are: (1) "Humans Use a ToM Module, So AI Systems Should As Well". (2) "Every Social Interaction Requires (Advanced) ToM". (3) "All ToM is the Same". (4) "Current Systems Already Have ToM". After discussing the misconception, we end each section by providing tentative guidelines on how the misconception can be overcome.
Injecting Conflict Situations in Autonomous Driving Simulation using CARLA
Venue: arXiv cs.HC 2025
Year: 2025

Simulation of conflict situations for autonomous driving research is crucial for understanding and managing interactions between Automated Vehicles (AVs) and human drivers. This paper presents a set of exemplary conflict scenarios in CARLA that arise in shared autonomy settings, where both AVs and human drivers must navigate complex traffic environments. We explore various conflict situations, focusing on the impact of driver behavior and decision-making processes on overall traffic safety and efficiency. We build a simple extendable toolkit for situation awareness research, in which the implemented conflicts can be demonstrated.
Accodemy: AI Powered Code Learning Platform to Assist Novice Programmers in Overcoming the Fear of Coding
Venue: arXiv cs.HC 2025
Year: 2025

Computer programming represents a rapidly evolving and sought-after career path in the 21st century. Nevertheless, novice learners may find the process intimidating for several reasons, such as limited and highly competitive career opportunities, peer and parental pressure for academic success, and course difficulties. These factors frequently contribute to anxiety and eventual dropout as a result of fear. Furthermore, research has demonstrated that beginners are significantly deterred by the fear of failure, which results in programming anxiety and and a sense of being overwhelmed by intricate topics, ultimately leading to dropping out. This project undertakes an exploration beyond the scope of conventional code learning platforms by identifying and utilising effective and personalised strategies of learning. The proposed solution incorporates features such as AI-generated challenging questions, mindfulness quotes, and tips to motivate users, along with an AI chatbot that functions as a motivational aid. In addition, the suggested solution integrates personalized roadmaps and gamification elements to maintain user involvement. The project aims to systematically monitor the progress of novice programmers and enhance their knowledge of coding with a personalised, revised curriculum to help mitigate the fear of coding and boost confidence.
VocalEyes: Enhancing Environmental Perception for the Visually Impaired through Vision-Language Models and Distance-Aware Object Detection
Venue: arXiv cs.HC 2025
Year: 2025

With an increasing demand for assistive technologies that promote the independence and mobility of visually impaired people, this study suggests an innovative real-time system that gives audio descriptions of a user's surroundings to improve situational awareness. The system acquires live video input and processes it with a quantized and fine-tuned Florence-2 big model, adjusted to 4-bit accuracy for efficient operation on low-power edge devices such as the NVIDIA Jetson Orin Nano. By transforming the video signal into frames with a 5-frame latency, the model provides rapid and contextually pertinent descriptions of objects, pedestrians, and barriers, together with their estimated distances. The system employs Parler TTS Mini, a lightweight and adaptable Text-to-Speech (TTS) solution, for efficient audio feedback. It accommodates 34 distinct speaker types and enables customization of speech tone, pace, and style to suit user requirements. This study examines the quantization and fine-tuning techniques utilized to modify the Florence-2 model for this application, illustrating how the integration of a compact model architecture with a versatile TTS component improves real-time performance and user experience. The proposed system is assessed based on its accuracy, efficiency, and usefulness, providing a viable option to aid vision-impaired users in navigating their surroundings securely and successfully.
AIDetection: A Generative AI Detection Tool for Educators Using Syntactic Matching of Common ASCII Characters As Potential 'AI Traces' Within Users' Internet Browser
Venue: arXiv cs.HC 2025
Year: 2025

This paper introduces a simple JavaScript-based web application designed to assist educators in detecting AI-generated content in student essays and written assignments. Unlike existing AI detection tools that rely on obfuscated machine learning models, AIDetection.info employs a heuristic-based approach to identify common syntactic traces left by generative AI models, such as ChatGPT, Claude, Grok, DeepSeek, Gemini, Llama/Meta, Microsoft Copilot, Grammarly AI, and other text-generating models and wrapper applications. The tool scans documents in bulk for potential AI artifacts, as well as AI citations and acknowledgments, and provides a visual summary with downloadable Excel and CSV reports. This article details its methodology, functionalities, limitations, and applications within educational settings.
Enhancing Post-Merger Integration Planning through AI-Assisted Dependency Analysis and Path Generation
Venue: arXiv cs.HC 2025
Year: 2025

Post-merger integration (PMI) planning presents significant challenges due to the complex interdependencies between integration initiatives and their associated synergies. While dependency-based planning approaches offer valuable frameworks, practitioners often become anchored to specific integration paths without systematically exploring alternative solutions. This research introduces a novel AI-assisted tool designed to expand and enhance the exploration of viable integration planning options. The proposed system leverages a frontier model-based agent augmented with specialized reasoning techniques to map and analyze dependencies between integration plan elements. Through a chain-of-thought planning approach, the tool guides users in systematically exploring the integration planning space, helping identify and evaluate alternative paths that might otherwise remain unconsidered. In an initial evaluation using a simulated case study, participants using the tool identified 43% more viable integration planning options compared to the control group. While the quality of generated options showed improvement, the effect size was modest. These preliminary results suggest promising potential for AI-assisted tools in enhancing the systematic exploration of PMI planning alternatives. This early-stage research contributes to both the theoretical understanding of AI-assisted planning in complex organizational contexts and the practical development of tools to support PMI planning. Future work will focus on refining the underlying models and expanding the evaluation scope to real-world integration scenarios.
Combating the Effects of Cyber-Psychosis: Using Object Security to Facilitate Critical Thinking
Venue: arXiv cs.HC 2025
Year: 2025

Humanity is currently facing an existential crisis about the nature of truth and reality driven by the availability of information online which overloads and overwhelms our cognitive capabilities, which we call Cyber-Psychosis. The results of this Cyber-Psychosis include the decline of critical thinking coupled with deceptive influences on the Internet which have become so prolific that they are challenging our ability to form a shared understanding of reality in either the digital or physical world. Fundamental to mending our fractured digital universe is establishing the ability to know where a digital object (i.e. a piece of information like text, audio, or video) came from, whether it was modified, what it is derived from, where it has been circulated, and what (if any) lifetime that information should have. Furthermore, we argue that on-by-default object security for genuine objects will provide the necessary grounding to support critical thinking and rational online behavior, even with the ubiquity of deceptive content. To this end, we propose that the Internet needs an object security service layer. This proposition may not be as distant as it may first seem. Through an examination of several venerable (and new) protocols, we show how pieces of this problem have already been addressed. While interdisciplinary research will be key to properly crafting the architectural changes needed, here we propose an approach for how we can already use fallow protections to begin turning the tide of this emerging Cyber-Psychosis today!
Token-Level Uncertainty-Aware Objective for Language Model Post-Training
Venue: arXiv cs.CL 2025
Year: 2025

In the current work, we connect token-level uncertainty in causal language modeling to two types of training objectives: 1) masked maximum likelihood (MLE), 2) self-distillation. We show that masked MLE is effective in reducing epistemic uncertainty, and serve as an effective token-level automatic curriculum learning technique. However, masked MLE is prone to overfitting and requires self-distillation regularization to improve or maintain performance on out-of-distribution tasks. We demonstrate significant performance gain via the proposed training objective - combined masked MLE and self-distillation - across multiple architectures (Gemma, LLaMA, Phi) and datasets (Alpaca, ShareGPT, GSM8K), mitigating overfitting while maintaining adaptability during post-training. Our findings suggest that uncertainty-aware training provides an effective mechanism for enhancing language model training.